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Bulk structural information from density functionals for patchy particles.

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Density functional theory accurately models tetravalent associating particles, but struggles with amorphous network formation. Modifications improve predictions, outperforming other density functional approaches.

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Area of Science:

  • Physical Chemistry
  • Soft Matter Physics
  • Computational Chemistry

Background:

  • Classical density functional theory (DFT) is crucial for understanding fluid behavior.
  • Wertheim's thermodynamic perturbation theory provides a foundation for associating particle systems.
  • Accurate modeling of bulk structural properties is essential for predicting material behavior.

Purpose of the Study:

  • To investigate the bulk structural properties of tetravalent associating particles using DFT.
  • To compare DFT predictions with results from computer simulations.
  • To evaluate the performance of different DFT functionals for these systems.

Main Methods:

  • Utilizing classical density functional theory, building upon Wertheim's thermodynamic perturbation theory.
  • Calculating density profiles within an effective test-particle geometry.
  • Comparing theoretical results with radial distribution functions from computer simulations.

Main Results:

  • A modified Yu and Wu functional based on fundamental measure theory shows accurate results for bulk properties.
  • Quantitative differences arise at low temperatures due to DFT's lack of geometrical information on patch arrangements.
  • The modified theory better fits simulations of the floating-bond model compared to amorphous tetrahedral network formation.
  • A Segura et al. DFT approach fails to capture fundamental structural properties.

Conclusions:

  • Modified DFT functionals can accurately describe bulk structural properties of tetravalent associating particles.
  • Limitations exist in current DFT approaches for modeling low-temperature amorphous network structures.
  • The choice of DFT functional significantly impacts the accuracy of simulation predictions.